An Efficient ensemble of Brain Tumour Segmentation and Classification using Machine Learning and Deep Learning based Inception Networks

Abstract

In recent times, Brain Tumor (BT) has become a common phenomenon affecting almost all age group of people. Identification of this deadly disease using computer tomography, magnetic resonance imaging are very popular now-a-days. Developing a Computer Aided Design (CAD) tool for diagnosis and classification of BT has become vital. This paper focuses on designing a tool for diagnosis and classification of BT using Deep Learning (DL) models, which involves a series of steps via acquiring (CT) image, pre-processing, segmenting and classifying to identify the type of tumor using SIFT with DL based Inception network model. The proposed model uses fuzzy C means algorithm for segmenting area of interest from the BT image acquired. Techniques like Gaussian Naïve Bayes (GNB) and logistic regression (LR) are used for classification processes. To ascertain all the techniques for its efficiency a benchmark dataset was used. The simulation outcome ensured that the performance of the proposed method with maximum sensitivity of 100%, specificity of 97.41% and accuracy of 97.96%.